Data Engineer
Job Title: Data Engineer
Location: Remote
Headquarters: Dallas, USA
Development Center: Ahmedabad
Job Summary:We are seeking a Data Engineer with strong SQL and programming skills (Python/Scala/Java) to design, build, and optimize scalable data pipelines and data platforms. The ideal candidate has experience with ETL/ELT frameworks, distributed processing technologies such as Spark, and cloud-based data services. You will collaborate with cross-functional teams to deliver reliable, high-quality data solutions while ensuring performance, governance, and operational excellence.
Experience: 2 years
Core technical skills
SQL proficiency (non-negotiable; look for window functions, query optimization, not just basic SELECTs)
At least one strong programming language (Python or Scala/Java)
Data pipeline/ETL-ELT tools (Airflow, dbt, Dagster, Luigi)
Distributed processing frameworks (Spark, Flink, sometimes Hadoop for legacy)
Cloud platforms (AWS, GCP, or Azure) and their data services (Redshift, BigQuery, Snowflake, Databricks)
Data modeling & architecture
Understanding of dimensional modeling, normalization, star/snowflake schemas
Experience with both batch and streaming architectures
Knowledge of data warehousing vs. data lake vs. lakehouse concepts
Familiarity with file formats (Parquet, Avro, ORC) and why they matter
Engineering fundamentals (separates good from average)
Version control (Git), CI/CD practices
Code quality: testing, modularity, documentation
Infrastructure-as-code (Terraform) and containerization (Docker, Kubernetes)
Understanding of data quality, observability, and monitoring
Signals of depth vs. surface knowledge
Can they explain why they chose a tool, not just that they used it?
Evidence of handling scale (data volumes, latency requirements, cost optimization)
Ownership of end-to-end pipelines vs. only writing isolated scripts
Awareness of data governance, lineage, and privacy/compliance
Soft signals
Collaboration with analysts, data scientists, and business stakeholders
Ability to translate ambiguous requirements into reliable pipelines
Communication clarity in how they describe past work
Watch-outs
Buzzword stuffing without depth (lists every tool but can’t go deep on any)
Only coursework/certifications with no applied projects
Confusing data engineering with data analysis or pure ML work
Why Solvative?
Top-of-the-line Apple laptops for increased mobility and better productivity.
Medical insurance for all permanent employees.
The opportunity of working with an organization that believes in investing in employees’ growth.
An informal work environment that enables you to have fun while being productive.
Lots and lots of fun activities: we take over one of the nearby restaurants every last Friday of the month, tickets to all Marvel movies for the entire team, company picnics, and more!
Location: Remote
Headquarters: Dallas, USA
Development Center: Ahmedabad
Job Summary:We are seeking a Data Engineer with strong SQL and programming skills (Python/Scala/Java) to design, build, and optimize scalable data pipelines and data platforms. The ideal candidate has experience with ETL/ELT frameworks, distributed processing technologies such as Spark, and cloud-based data services. You will collaborate with cross-functional teams to deliver reliable, high-quality data solutions while ensuring performance, governance, and operational excellence.
Experience: 2 years
Core technical skills
SQL proficiency (non-negotiable; look for window functions, query optimization, not just basic SELECTs)
At least one strong programming language (Python or Scala/Java)
Data pipeline/ETL-ELT tools (Airflow, dbt, Dagster, Luigi)
Distributed processing frameworks (Spark, Flink, sometimes Hadoop for legacy)
Cloud platforms (AWS, GCP, or Azure) and their data services (Redshift, BigQuery, Snowflake, Databricks)
Data modeling & architecture
Understanding of dimensional modeling, normalization, star/snowflake schemas
Experience with both batch and streaming architectures
Knowledge of data warehousing vs. data lake vs. lakehouse concepts
Familiarity with file formats (Parquet, Avro, ORC) and why they matter
Engineering fundamentals (separates good from average)
Version control (Git), CI/CD practices
Code quality: testing, modularity, documentation
Infrastructure-as-code (Terraform) and containerization (Docker, Kubernetes)
Understanding of data quality, observability, and monitoring
Signals of depth vs. surface knowledge
Can they explain why they chose a tool, not just that they used it?
Evidence of handling scale (data volumes, latency requirements, cost optimization)
Ownership of end-to-end pipelines vs. only writing isolated scripts
Awareness of data governance, lineage, and privacy/compliance
Soft signals
Collaboration with analysts, data scientists, and business stakeholders
Ability to translate ambiguous requirements into reliable pipelines
Communication clarity in how they describe past work
Watch-outs
Buzzword stuffing without depth (lists every tool but can’t go deep on any)
Only coursework/certifications with no applied projects
Confusing data engineering with data analysis or pure ML work
Why Solvative?
Top-of-the-line Apple laptops for increased mobility and better productivity.
Medical insurance for all permanent employees.
The opportunity of working with an organization that believes in investing in employees’ growth.
An informal work environment that enables you to have fun while being productive.
Lots and lots of fun activities: we take over one of the nearby restaurants every last Friday of the month, tickets to all Marvel movies for the entire team, company picnics, and more!